Map regression is the process of working backwards from later maps to earlier maps of the same area, to determine change or to locate past features. The...

particular kinds of archaeological materials if the theory is true. Map regression, comparing maps from different periods of the same area, can reveal past structures...

combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) estimates the parameters of a logistic model...

regression; a model with two or more explanatory variables is a multiple linear regression. This term is distinct from multivariate linear regression...

In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than...

applied statistics and geostatistics, regression-kriging (RK) is a spatial prediction technique that combines a regression of the dependent variable on auxiliary...

Ridge regression (also known as Tikhonov regularization, named for Andrey Tikhonov) is a method of estimating the coefficients of multiple-regression models...

predictive performance than other linear models, such as logistic regression and linear regression. Classifying data is a common task in machine learning. Suppose...

fields. Logistic map, a nonlinear recurrence relation that plays a prominent role in chaos theory Logistic regression, a regression technique that transforms...

trackers. This allows regression based models to be very efficient in crowded pictures; if the density per pixel is very high regression models are best suited...

PMID 2180307. Medline Plus. Caudal Regression Syndrome.https://medlineplus.gov/genetics/condition/caudal-regression-syndrome/#frequency Al Kaissi, Ali;...

the preference datum. Like all regression methods, the computer fits weights to best predict data. The resultant regression line is referred to as an ideal...

(See also multivariate adaptive regression splines.) Penalized splines. This combines the reduced knots of regression splines, with the roughness penalty...

used for estimating the unknown regression coefficients in a standard linear regression model. In PCR, instead of regressing the dependent variable on the...

Great Plumstead (52°38′N 1°22′E / 52.63°N 1.37°E / 52.63; 1.37). A map regression analysis published by the Council for British Archaeology supports Carter's...

multilevel regression with poststratification model involves the following pair of steps: MRP step 1 (multilevel regression): The multilevel regression model...

A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically...

model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that sense it is...

simple function (also called regression). The main difference between regression and interpolation is that polynomial regression gives a single polynomial...

(GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the...

nearest neighbor. The k-NN algorithm can also be generalized for regression. In k-NN regression, also known as nearest neighbor smoothing, the output is the...

breaking governments pictures. Fernández, Antonio (2011). "A Generalized Regression Methodology for Bivariate Heteroscedastic Data" (PDF). Communications...

pseudoinverse. The regression equations are called "ridgeless" because they lack a ridge regularization term. In this view, linear regression is a special case...

to estimate a mixture of gaussians, or to solve the multiple linear regression problem. The EM algorithm was explained and given its name in a classic...

In philosophy, Ryle's regress is a classic argument against cognitivist theories, and concludes that such theories are essentially meaningless as they...

process prior is known as Gaussian process regression, or kriging; extending Gaussian process regression to multiple target variables is known as cokriging...

zur Algebra und Geometrie. 43 (1): 297–302. arXiv:math/0009026. MR 1913786. A calculator for piecewise regression. A calculator for partial regression....

independent variables. Multivariate logistic regression uses a formula similar to univariate logistic regression, but with multiple independent variables...

to implicitly map input variables to higher-dimensional space. Multivariate linear regression extends the concept of linear regression to handle multiple...

not causal. This use of the word "regression" was coined by Sir Francis Galton in a study from 1885 called "Regression Toward Mediocrity in Hereditary Stature"...